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Ecological Indicators 60 (2016) 832–844

Contents lists available at ScienceDirect

Ecological Indicators

jo ur nal ho me page: www.elsev ier .com/ locate / ecol ind

o factors describing forest naturalness predict the occurrence andbundance of middle spotted woodpecker in different forestandscapes?

rystyna Stachura-Skierczynskaa, Ziemowit Kosinskib,∗

Laboratory of Biological Spatial Informations, Faculty of Biology, Adam Mickiewicz University, Umultowska 89, 61-614 Poznan, PolandDepartment of Avian Biology and Ecology, Institute of Environmental Biology, Faculty of Biology, Adam Mickiewicz University, Umultowska 89, 61-614oznan, Poland

r t i c l e i n f o

rticle history:eceived 9 August 2014eceived in revised form 26 July 2015ccepted 11 August 2015

eywords:iological indicatorsorest naturalnessiddle spotted woodpecker

eiopicus medius

a b s t r a c t

Conservation biologists often use some specialized species as surrogates for communicating conser-vation needs, e.g. to signal states and changes in ecosystem. This requires a detailed knowledge of aspecies’ habitat demands and relationship between its occurrence and abundance, and certain envi-ronmental conditions. This paper explores the relationship between the occurrence and abundance ofmiddle spotted woodpecker (Leiopicus medius) and structural, compositional, and functional elementsof forest naturalness in three different forest landscapes in Poland, which encompass a wide spectrum ofspecies’ habitats. Neither compositional nor functional elements of forest naturalness seemed to affectspecies’ distribution. In all studied areas, environmental variables related to the structural elements offorest naturalness, e.g. the share of old and uneven-aged stands, number of large living trees, positively

influenced the occurrence and abundance of middle spotted woodpecker. Mature, unevenly structuredforests might occur as a result of sustainable forest management, aimed at preserving the continuity ofold stands and the maintenance of diverse age and species’ structure, providing suitable habitat condi-tion for the species. Therefore, both presence and abundance of middle spotted woodpeckers can serveas indicators of wildlife-friendly forest management in deciduous forests.

. Introduction

The ambiguous term “forest naturalness” is still one of the mainroblems in studying the relationship between forest-dependentpecies and their environment. According to Winter (2012), natu-alness can be either the state of ecosystem undisturbed by humansr the difference (often referred to as the degree of naturalness)etween the primeval and the current state. The main problem withhe former approach is the lack of reference point—forest ecosys-ems truly undisturbed by human activities (Chiarucci et al., 2010;arviainen, 2005; Winter, 2012). The latter is often used in for-st conservation strategies to assess the ecological value of forestcosystems (McRoberts et al., 2012).

The degree of forest naturalness can be analyzed at three lev-

ls: compositional, structural, and functional level of ecosystemRoberge et al., 2008). Compositional level refers to the occurrencef elements that are characteristic of forests with a high degree

∗ Corresponding author. Tel.: +48 61 8295617; fax: +48 61 8295636.E-mail address: zkosinsk@amu.edu.pl (Z. Kosinski).

ttp://dx.doi.org/10.1016/j.ecolind.2015.08.020470-160X/© 2015 Elsevier Ltd. All rights reserved.

© 2015 Elsevier Ltd. All rights reserved.

of naturalness, e.g. high number of native tree species and deadwood diversity. Structural level relates to the spatial configura-tion of the elements and their quantities, e.g. diversification of theage and vertical structure of stands, and microhabitats such as hol-lows and giant trees. Functional level concerns the processes thatare characteristic for natural dynamics, disturbances, and succes-sion of the forest ecosystems, and to the anthropogenic change(Angelstam et al., 2004; Brumelis et al., 2011; Bütler et al., 2004;Korpilahti and Kuuluvainen, 2002; Kurlavicius et al., 2004). Theabove-described conditions might be interrelated, i.e. natural dis-turbances serve as a driving factor of uneven stand structure; thepresence of trees of various age and size results in high diversitydead wood of different types, and so forth. Yet despite many stud-ies on forest naturalness, these criteria are still not harmonizedwith forest inventory database and management plans (Korpilahtiand Kuuluvainen, 2002; Winter, 2012). The possible solution to thisproblem is to use only selected criteria that can be translated into

quantifiable indicators based on available datasets ready-to-use orfield measurements designed for the specific purpose of study.

Conservation biologists often use some specialized species assurrogates for communicating conservation needs. Some species

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K. Stachura-Skierczynska, Z. Kosinski

ave particular attributes that may enable them to signal changes incosystems, e.g. extent of various types of anthropogenic influence,opulation trends in other organisms, or to locate areas of highiodiversity (Caro and O’Doherty, 1999). Among forest-dependentirds, several woodpecker species are assessed as indicator speciesue to their dependence on habitat components typical for naturalorests. They require large home ranges and often depend on deadnd large trees for nesting and foraging (Mikusinski and Angelstam,004). Most woodpeckers excavate holes, especially in dying orecayed trees (Kosinski and Kempa, 2007; Wesołowski, 2007).ikusinski and Angelstam (1997) demonstrated that the history of

orest use and the level of economic development have affected theccurrence of specialized woodpecker species throughout Europe.his relationship was confirmed by several studies (Angelstamt al., 2003; Roberge et al., 2008; Spitznagel, 1990). The occurrencef particular woodpecker species and woodpecker richness haseen shown to be excellent indicators of species’ richness and/orbundances of other forest birds (Drever et al., 2008; Mikusinskit al., 2001; Roberge and Angelstam, 2006).

The effective indicators assessing changes in habitat (e.g. for-st naturalness), called “health indicators,” are likely to be small,esident species, restricted to certain habitats, and with wideeographic range (Caro and O’Doherty, 1999). The middle-sized20–22 cm; wing-span 33–34 cm), non-migratory middle spottedoodpecker (Leiopicus medius), distributed over large parts of

urope from NW Spain to western Russia and from the coasts of theediterranean to the North and Baltic Sea (Pasinelli, 2003), is con-

idered as one of the best indicators of forest naturalness (Roberget al., 2008). This species is associated with mature, rough-barkedeciduous stands (Hertel, 2003; Kosinski, 2006; Pasinelli, 2003).oreover, middle spotted woodpecker often uses polyphore-

nfested trees for excavating holes (Kosinski and Winiecki, 2004;asinelli, 2007). These studies suggest that some habitat compo-ents associated with forest naturalness might have an impactn the presence of middle spotted woodpecker. However, Landrest al. (1988) noticed that although geographically separated habi-ats appear similar, in fact, they might vary, e.g. in vegetationtructure, habitat, and resource patchiness, and natural disturbanceegimes. Such discrepancies in habitat features might affect indi-ator species’ responses. Several studies found that middle spottedoodpecker exhibited a certain degree of flexibility in habitat use,tilizing other habitats than mature oak stands, i.e. beech and aldertands, commonly associated with its occurrence (Hertel, 2003;asinelli, 2003; Stachura-Skierczynska and Kosinski, 2014; Weiß,003; Winter et al., 2005). However, it might be speculated that

ts occurrence and abundance in geographically separated areasepend on similar structural characteristics of forest stands. Earlyesearch that assessed the relationship between the presence ofhe middle spotted woodpecker and the degree of naturalness ofhe forests was restricted to two regions in the European hemibo-eal zone (Roberge et al., 2008). However, to achieve knowledgehat could be widely applicable to solve conservation problems,.g. use the middle spotted woodpecker as an effective indicator oforest naturalness with wide geographic range, studies of habitatelection should be replicated across the geographically separatedabitats within the species’ range (Roberge et al., 2008). In thistudy, we analyzed the relationship between the occurrence andbundance of middle spotted woodpecker and forest naturalnessithin the species’ continuous range in different types of forest

andscapes. All studies areas were located in the zone of temperateeciduous forests encompassing a wide spectrum of woodpeckerabitats, mostly in productive forests characterized by long history

f use and different degree of human alteration.

In this article, we focus on the following questions: (1) Whichactors related to forest naturalness affect the presence and abun-ance of middle spotted woodpeckers? (2) Do geographically

ogical Indicators 60 (2016) 832–844 833

separated habitats show the same pattern of species’ responseto factors describing forest naturalness? Finally, we discuss theusefulness of middle spotted woodpecker as an indicator of for-est naturalness according to the knowledge about species’ habitatrequirements.

2. Material and methods

2.1. Study area

Studies were performed in Poland, in three NATURA 2000 Spe-cial Protection Area sites (Fig. 1), differing with regard to dominantforest types, history, and intensity of management use: Kroto-szyn Oak Forest (KOF), Forest at the Drawa River (FDR), andKnyszyn Forest (KF). All studied areas were located in the deciduousforest zone, which encompass a wide spectrum of potential wood-pecker habitats: oak-dominated old growths, oak-beech nemoralforests, deciduous forests with maple and lime and alder bogs.KOF (17◦32′17′′E, 51◦38′36′′N) consists of several small isolatedforest patches of about 15,600 ha in total, surrounded by agricul-tural areas. Stands dominated by pedunculate oak (Quercus robur)cover about 7400 ha (47% of total forest cover). Almost 66% of oak-dominated stands are over 100 years old, which signifies that KOFhas the largest concentration of old oak forests in Central Europe(Kasprowicz, 2010). Most of oak stands are intensively managedwith clear-cutting (about 51% of all cutting area), except for fewreserves covering less than 1% of total forest area. FDR (15◦53′37′′E,53◦02′32′′N) covers about 115,000 ha of forest land. Scots pine(Pinus sylvestris) is a dominant species, mainly on sandy soils,abandoned fields and grasslands, and sites primarily covered bybroadleaved and mixed forests that were destroyed during wars.Deciduous stands, mainly with oak and beech (Fagus sylvatica) orash (Fraxinus excelsior) and alder (Alnus glutinosa), along watercourses are located mostly in the southern part of the area cov-ering about 11,200 ha (9.7% of forest land); however, stands withoak as a dominant species constitute less than 5000 ha (4.3% oftotal forest cover). Beech and oak stands are usually managed withshelterwood. In total, clear-cutting constitutes about 29% of allcutting area. Protected forests, including the Drawienski NationalPark, cover about 9% of the total forest area. KF (23◦24′18′′E,53◦03′23′′N) encompasses about 92,000 ha of forests. Pine is thedominant species, followed by spruce (Picea abies), birch (Birch sp.),and alder. KF has been an important timber growing area since the16th century, with several episodes of wanton exploitation duringwars (Czerwinski, 1995). Deciduous stands cover approximately12,000 ha (13% of forest land), but stands with dominating oakmake only 3400 ha. The most common management practices indeciduous forests are clear-cutting (alder bogs) and selective cut-ting with long rotation period (species-rich deciduous stands). Intotal, clear-cutting constitutes about 24% of the total cutting area.Protected reserves cover approximately 4% of the total forest area.More detailed characteristics of forest stands are given elsewhere(Stachura-Skierczynska and Kosinski, 2014).

2.2. Woodpecker occurrence data

Woodpeckers were counted on 1 km2 sample plots, selectedat random, during pre-breeding periods (from the second half ofMarch until the end of April) in 2010 (KOF), 2010–2011 (FDR),and 2011–2012 (KF) (Stachura-Skierczynska and Kosinski, 2014).Two regions (FDR and KF) were surveyed in two years due to the

large number of sample plots. During the long-term monitoring ofmiddle spotted woodpeckers (15 years) on two large study plotsin western Poland, we found that the number of territories var-ied slightly between years (coefficient of variation CV = 7.4 and

834 K. Stachura-Skierczynska, Z. Kosinski / Ecological Indicators 60 (2016) 832–844

F Foress

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ig. 1. Distribution of study sites across Poland. KOF – Krotoszyn Oak Forest, FDR –potted woodpeckers; empty square – unoccupied plot.

1.9, respectively) (Z. Kosinski et al. unpubl. data). Therefore, wessumed that data obtained in different years and study plots areomparable. In this study, we used data from 19 plots in KOF, 56n FDR, and 40 in KF. Plots were selected under the condition thatach one should contain at least 20 ha (approximately the year-round size of home range—Pasinelli et al., 2001) of deciduousorest. In this way, each of our sample plots was at least partiallyovered by deciduous stands potentially suitable for middle spottedoodpecker. Within plots, all potential woodpecker habitats were

hecked twice. All counts were based on the same call-playbackethod described thoroughly by Kosinski et al. (2004). To provoke

esponses from territorial birds, we used taped calls (rattle- anddvertising-calls of males). To reduce the probability of some indi-iduals being attracted away from their territories through the usef the playback technique, we applied the following parameters:

minimum distance of 150–200 m between points, a maximumime of 40 s for the stimulation in one bout, and a minimum inter-al of 1–2 min for listening. A second bout of playback was rarelyerformed. After the first bird’s response, the taped calls weretopped to determine the initial location of the bird. We recordednd mapped the woodpecker’s position, behavior, type of call,nd direction of movements. We paid special attention to regis-er simultaneously active birds. In this way, the number of birdsolding territory on the study plots could be assessed. We usehe term “territory” because middle spotted woodpeckers defendlmost exclusive territories during March and April, with both sexesarticipating to similar degrees in territorial defense (Pasinelli et al.,001). On the basis of our long-term experience, we assumed that

wo registrations in pre-breeding period were sufficient to accept

territory (Z. Kosinski unpubl. data). Moreover, in this way, it wasossible to exclude a transient/floater birds (Pasinelli et al., 2001;obles and Olea, 2003; Robles et al., 2008). Earlier studies suggest

t at the Drawa River, KF – Knyszyn Forest. Black square – plot occupied by middle

that up to 80% of all territories can be found during one visit (e.g.Kosinski and Winiecki, 2003; Kosinski et al., 2004), and two visitsallow an experienced observer to find almost 100% of territoriesin pre-breeding period (Z. Kosinski unpubl. data). Moreover, ourearlier findings suggest that distribution of nest-holes correspondswith the territory delineated during March and April (Kosinski andKsit, 2006; Kosinski and Winiecki, 2003; Kosinski et al., 2004; Z.Kosinski unpubl. data). In this way, 70, 49, and 46 woodpecker ter-ritories were found in KOF, FDR, and KF, respectively. A plot wasassumed as occupied if it contained at least one territory.

2.3. Assessment of forest naturalness

Data describing forest naturalness were obtained in the fieldbetween May and August. In addition, some attributes wereextracted from the forest inventory database (consisting of stand-level forest numeric map and attribute tables describing, amongother, the structure and composition of stands). We adopted andmodified the framework for variables’ selection after Roberge et al.(2008), identifying three main components of forest naturalness:structural, compositional, and functional (Table 1). Structural vari-ables were associated with the diversity of age structure withinstands and described share of old growth stands, share of uneven-aged structure, the number of large living trees, and the number ofmicrohabitat trees. Compositional variables represented the vari-ety of forest tree species and number of snags. Functional variables,i.e. number of cut stamps and wet habitats, were related to the nat-ural function of forest ecosystem and to the anthropogenic change

(McRoberts et al., 2012; Roberge et al., 2008). Wet habitats, forexample, are very likely those with limited use due to habitat con-dition, with some of them even excluded from use (temporarily orpermanently). Share of old growth stand, uneven-aged structure,

K. Stachura-Skierczynska, Z. Kosinski / Ecological Indicators 60 (2016) 832–844 835

Table 1Characteristics of variables describing forest naturalness in study areas. KOF – Krotoszyn Oak Forest, FDR – Forest at the Drawa River, KF – Knyszyn Forest.

Variable KOF (N = 19)Mean ± SE

FDR (N = 56)Mean ± SE

KF (N = 40)Mean ± SE

Structural variablesOld growth (% share per sample plot) 9.7 ± 2.7 11.6 ± 2.0 12.3 ± 1.9Uneven-aged stands (% share per sample plot) 44.0 ± 5.4 19.8 ± 2.4 21.1 ± 2.1Number of large living trees (no./ha) 35.1 ± 3.7 26.4 ± 2.3 17.7 ± 1.6Number of microhabitat trees (no./ha) 7.8 ± 0.8 1.6 ± 0.3 0.5 ± 0.2

Compositional variablesNumber of tree species (no./ha) 8.8 ± 0.5 7.4 ± 0.4 9.0 ± 0.4Number of snags (no./ha) 7.1 ± 1.0 2.8 ± 0.4 5.2 ± 0.8

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for each model was assessed using Akaike’s Information Criterioncorrected for small sample size (AICc) (Burnham and Anderson,2002). Models with the lowest AICc were better supported by thedata, i.e. explained more variance with the fewest parameters. The

Table 2Candidate model set for the analysis of relationship between the presence andabundance of middle spotted woodpecker, and forest naturalness.

Model Model function

1 Intercept-only model2 Number of large living trees3 Number of large living trees + Number of microhabitat

trees4 Number of large living trees + Old growth5 Number of large living trees + Old growth + Uneven-aged

stands6 Old growth + Uneven-aged stands7 Old growth + Uneven-aged stands + Number of large living

trees + Number of microhabitat trees8 Number of tree species9 Number of tree species + Number of snags10 Number of snags11 Number of cut stamps12 Wet habitats13 Number of cut stamps + Wet habitats14 Old growth + Uneven-aged stands + Number of large living

trees + Number of microhabitat trees + Number of snags15 Number of large living trees + Number of microhabitat

trees + Number of snags

Functional variablesNumber of cut stumps (no./ha) 83.9 ±

Wet habitats (% share per sample plot) 5.5 ±

nd wet habitats were calculated as the total percentage share oftands fulfilling the given condition per 1 km2 plot. We assumedtands as “old growth” where the age of dominant species exceededhe cutting age for this species in the region. The age limits wereet according to the local condition and the information providedy the local forestry service. Exemplary values were: for oak—120ears in FDR and KF and 180 years in KOF, for other hardwoodpecies—100–120 years in all study areas, and for alder—80 years inll study areas. Stands at least 80 years old with at least 30 years ofge difference between trees were assumed as uneven-aged standsKurlavicius et al., 2004; modified). According to the forest inven-ory database, we assumed bog and riparian forest types for wetabitats. Other variables were measured on each 1 km2 plot, in0 sampling points located 150–200 m from each other, with anxception of stands younger than 20 years and clear-cutting. Atach point, a rectangular polygon, 50 m long and 10 m wide, wasemarcated using the tape measure (Bobiec, 2002; modified). Thezimuth of the polygon axis changed clockwise in order to ran-omize the sample (N–NE–E–SE–S–SW–W–NW). The minimumistance between samples was applied in order to minimize spatialutocorrelation in datasets. In total, 1 ha of forest area was sam-led on each 1 km2 plot. In each sampling point, we measured theumber of large living trees of at least 50 cm diameter at breasteight (DBH), number of microhabitat trees (trees with holes, cavi-ies, and/or side necrosis), number of tree species, number of snagsith at least 14 cm in DBH (thinnest substrate that provides hole

xcavation; Kosinski and Ksit, 2007; Z. Kosinski unpubl. data), andumber of cut stumps. The number of tree species in the canopy

ayer was expressed as a cumulative number per hectare (ha). Otherariables were expressed as number per ha.

.4. Statistical analysis

The relationship between woodpeckers’ occurrence and forestaturalness was analyzed with generalized linear models (GLM),sing species’ occurrence (presence/absence) as a response vari-ble, binomial error distribution, and logit link function. GLM withoisson distribution was used to evaluate the relationship of theabitat characteristics to middle spotted woodpeckers’ abundance.

n case of KOF, due to small sample size of absence plots, onlyhe relationship between woodpeckers’ abundance and habitatharacteristics was performed. Results of multiple regression anal-sis may be affected by strong correlation among the explanatoryariables (Dormann et al., 2013; Menard, 1995). Therefore, the cor-elation structure of measured variables was investigated prior toLM analysis using correlation coefficients (Spearman rS and Pear-on r) and the variance inflation factor (VIF) (Appendix A). VIFs

easure how much variances of estimated regression coefficients

re inflated when compared to having uncorrelated predictorsDormann et al., 2013); VIF > 10 indicates that multicollinear-ty unduly affects the least square estimates of the regression

84.3 ± 4.2 80.5 ± 3.96.5 ± 2.2 13.1 ± 2.2

coefficients. Our variables were generally not strongly collinear(KOF—|rS| ≤ 0.65, |r| ≤ 0.65, VIF ≤ 3.03; FDR—|rS| ≤ 0.70, |r| ≤ 0.66,VIF ≤ 2.54; KF—|rS| ≤ 0.65, |r| ≤ 0.68, VIF ≤ 1.84; Appendix A), sug-gesting that they represented different attributes of naturalness.Moreover, VIF was used as a measure of the degree of multi-collinearity among independent variables in general linear models.None of the variables exceeded VIF value >6.04.

Following the information-theoretic approach (Burnham andAnderson, 2002), multimodel inference, and model averaging wereused to investigate how variables describing forest naturalnessexplained woodpeckers’ occurrence and abundance. We developeda set of 16 candidate models for each study site including thevariables which were biologically and ecologically relevant on thebasis of literature, which may explain the relationship betweenthe occurrence and abundance of middle spotted woodpeckersand forest naturalness (Table 2). Candidate models consisted ofthe intercept-only model (model 1) and 15 models with differentcombinations of structural (models 2–7), compositional (models8–10), and functional variables (models 11–13), and combinationsof structural and compositional variables, including the full modelwith all explanatory variables (models 14–16). The relative support

16 Old growth + Uneven-aged stands + Number of large livingtrees + Number of microhabitat trees + Number of treespecies + Number of snags + Number of cut stamps + Wethabitats

836 K. Stachura-Skierczynska, Z. Kosinski / Ecological Indicators 60 (2016) 832–844

Table 3Comparison of variables describing forest naturalness on plots occupied and unoccupied by middle spotted woodpeckers (Mann–Whitney U two-sample test with MonteCarlo exact test), in Krotoszyn Oak Forest (KOF), Forest at the Drawa River (FDR), and Knyszyn Forest (KF).

Variable OccupiedMean ± SE

UnoccupiedMean ± SE

Z P

KOF N = 17 N = 2Old growth 9.3 ± 2.8 13.6 ± 11.8 −0.399 0.690Uneven-aged stands 47.8 ± 5.2 11.1 ± 1.7 −2.258 0.024Number of large living trees 37.6 ± 3.5 13.0 ± 5.0 −1.729 0.084Number of microhabitat trees 8.0 ± 0.9 6.5 ± 0.5 −0.734 0.463Number of tree species 8.4 ± 0.5 12.0 ± 2.0 −1.962 0.048Number of snags 7.4 ± 1.1 4.0 ± 3.0 0.132 0.328Number of cut stumps 86.4 ± 6.0 63.0 ± 26.0 0.930 0.411Wet habitats 5.7 ± 2.6 3.4 ± 3.3 −0.545 0.586

FDR N = 20 N = 36Old growth 23.2 ± 4.0 5.2 ± 1.4 −3.776 <0.001Uneven-aged stands 26.9 ± 4.0 15.9 ± 2.7 −2.146 0.032Number of large living trees 37.2 ± 4.1 20.4 ± 2.3 −3.370 0.001Number of microhabitat trees 1.8 ± 0.4 1.5 ± 0.4 −1.720 0.085Number of tree species 7.8 ± 0.6 7.3 ± 0.5 0.983 0.335Number of snags 1.6 ± 0.4 3.5 ± 0.5 2.359 0.016Number of cut stumps 84.4 ± 7.0 84.3 ± 5.2 −0.428 0.676Wet habitats 5.9 ± 4.0 6.9 ± 2.7 −1.419 0.156

KF N = 17 N = 23Old growth 19.8 ± 3.5 6.8 ± 1.3 −3.434 0.001Uneven-aged stands 28.4 ± 3.4 15.6 ± 2.1 −2.886 0.004Number of large living trees 21.0 ± 2.9 15.2 ± 1.7 −1.274 0.203Number of microhabitat trees 0.5 ± 0.2 0.6 ± 0.3 −0.441 0.659Number of tree species 9.5 ± 0.5 8.6 ± 0.5 −1.444 0.151

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Number of snags 4.9 ± 1.0

Number of cut stumps 75.8 ± 6.5

Wet habitats 14.7 ± 3.2

ubset of models with substantial support (delta AICc ≤ 2 comparedo the best model) was selected, with their parameter estimatesnd standard errors averaged (model-averaging) (Barton, 2009;urnham and Anderson, 2002; Johnson and Omland, 2004). For allodels, Akaike weights were calculated to provide a measure of the

robability that a given model was the most strongly supportedn the model set, with higher weights indicating better explana-ory power. To assess the performance of woodpecker occurrence

odels, areas under ROC (receiver operating characteristics) curvesAUC) were calculated. All analyses and graphics were carried outsing R v.3.1.2 (R Development Core Team, 2014).

. Results

.1. Relationship between occurrence and habitat characteristics

Middle spotted woodpeckers were present in 17 plots in KOF, 20lots in FDR, and 17 plots in KF (Table 3). Plots occupied by middlepotted woodpeckers clearly contained more uneven-aged standsompared to unoccupied. The share of old growth stands was sig-ificantly larger in occupied plots in FDR and KF than in unoccupiedreas. Moreover, in FDR, plots occupied by middle spotted wood-eckers had higher number of large living trees than unoccupiedlots, while in KOF this difference was close to the margin of sta-istical significance. Furthermore, in FDR, plots occupied by middlepotted woodpeckers had significantly lower number of snags thann unoccupied areas. Also, the number of tree species in occupiedlots in KOF was considerably less than that in unoccupied plotsTable 3). It should be noted, however, that there were only twolots without woodpeckers in the sample from KOF.

In the case of FDR, three best models (models 4, 6, and 14;elta AICc ≤ 2 compared to the best model) accounted for most

kaike model weight (approximately 0.75), indicating they wereetter supported by the data than the remaining models (Table 4).he first-ranked model with an Akaike weight of 0.313 was only.5 times more likely than the third-ranked model with Akaike

5.4 ± 1.2 −1.069 0.94884.0 ± 4.9 −0.013 0.31711.9 ± 3.0 −0.671 0.502

weight of 0.214, and in this way, these models had approximatelyequal levels of support. These models included four structural vari-ables, i.e. number of large living trees, share of old growth anduneven-aged stands, number of microhabitat trees, and one com-positional variable—number of snags. As revealed by the ROC curvewith AUC value of 0.861, the averaged model performed well. Theparameters based on model-averaging show that the occurrenceprobability increased with increasing share of old growth (standsexceeding the average cutting age) and decreased with increas-ing number of snags with DBH ≥ 14 cm (Table 5, Fig. 2). It shouldbe mentioned that in case of FDR, we observed weak but insignif-icant positive correlation between the number of snags and theamount of pine-dominated stands (Spearman’s rank coefficientrS = 0.260; p = 0.056), and weak negative but statistically signifi-cant correlation with the amount of deciduous stands (rS = −0.329;p = 0.013). There were no such relationships in two remainingareas (KOF: rS = −0.055; p = 0.747 and rS = 0.122; p = 0.618; KF:rS = −0.033; p = 0.900 and rS = 0.109; p = 0.501, respectively).

In the case of KF, the best-supported model (model 6) includedtwo structural variables: share of old growth and share of uneven-aged stands (Akaike weight – 0.63) (Table 5, Fig. 2). The occurrenceprobability of woodpeckers increased with increasing of bothexplanatory variables. The best model yielded AUC value of 0.880.

3.2. Relationship between the number of territories and habitatcharacteristics

Species’ abundance varied between 0 and 9 terr./km2

(mean ± SD = 3.7 ± 2.5) in KOF, 0 and 5 terr./km2 (0.9 ± 1.4) in FDR,and 0 and 7 terr./km2 (1.2 ± 1.8) in KF. In the case of KOF, threemodels with substantial support (models 7, 5, and 6; delta AICc ≤ 2compared to the best model) accounted for most Akaike model

weight (approximately 0.94). The evidence ratio of the first-rankedmodel with an Akaike weight of 0.426 relative to that of the thirdmodel with an Akaike weight of 0.237 was 1.8. These three modelsincluded four structural variables of forest naturalness (Table 6).

K. Stachura-Skierczynska, Z. Kosinski / Ecological Indicators 60 (2016) 832–844 837

Table 4Model-selection statistic for the analysis of the occurrence of middle spotted woodpeckers in Forest at the Drawa River (FDR), and Knyszyn Forest (KF). The best models(separated by �AICc ≤2) are highlighted in bold.

Area Model Model function dfa AICcb �AICcc Weightd

FDR 4 Number of large living trees + Old growth 3 58.9 0.00 0.3136 Old growth + Uneven-aged stands 3 59.6 0.67 0.223

14 Old growth + Uneven-aged stands + Number of large living trees + Numberof microhabitat trees + Number of snags

6 59.8 0.90 0.214

5 Number of large living trees + Old growth + Uneven-aged stands 4 61.2 2.31 0.1067 Old growth + Uneven-aged stands + Number of large living trees + Number of

microhabitat trees5 63.6 4.64 0.033

15 Number of large living trees + Number of microhabitat trees + Number of snags 4 63.6 4.66 0.0332 Number of large living trees 2 64.2 5.29 0.0243 Number of large living trees + Number of microhabitat trees 3 66.4 7.51 0.008

16 Old growth + Uneven-aged stands + Number of large living trees + Number ofmicrohabitat trees + Number of tree species + Number of snags + Number of cutstamps + Wet habitats

9 67.3 8.42 0.005

10 Number of snags 2 69.6 10.67 0.0029 Number of tree species + Number of snags 3 70.3 11.39 0.0011 Intercept-only model 1 75.1 16.16 0.0008 Number of tree species 2 76.9 17.98 0.000

12 Wet habitats 2 77.2 18.27 0.00011 Number of cut stamps 2 77.2 18.31 0.00013 Number of cut stamps + Wet habitats 3 79.4 20.50 0.000

KF 6 Old growth + Uneven-aged stands 3 41.2 0.00 0.6325 Number of large living trees + Old growth + Uneven-aged stands 4 43.5 2.30 0.2014 Number of large living trees + Old growth 3 45.0 3.81 0.0947 Old growth + Uneven-aged stands + Number of large living trees + Number of

microhabitat trees5 46.1 4.85 0.056

14 Old growth + Uneven-aged stands + Number of large living trees + Number ofmicrohabitat trees + Number of snags

6 48.7 7.45 0.015

2 Number of large living trees 2 55.6 14.38 0.0001 Intercept-only model 1 56.7 15.42 0.000

16 Old growth + Uneven-aged stands + Number of large living trees + Number ofmicrohabitat trees + Number of tree species + Number of snags + Number of cutstamps + Wet habitats

9 57.0 15.80 0.000

8 Number of tree species 2 57.1 15.88 0.00011 Number of cut stamps 2 57.7 16.51 0.000

3 Number of large living trees + Number of microhabitat trees 3 57.9 16.70 0.00012 Wet habitats 2 58.5 17.22 0.00010 Number of snags 2 58.8 17.53 0.000

9 Number of tree species + Number of snags 3 59.5 18.22 0.00013 Number of cut stamps + Wet habitats 3 60.0 18.78 0.00015 Number of large living trees + Number of microhabitat trees + Number of snags 4 60.3 19.10 0.000

a Degrees of freedom.b Akaike’s information criterion.

ore pa

TsTi(

5vs

TMD

c Difference in AICc relative to the model with the lowest AICc.d Models are ranked according to their Akaike weight; higher weights indicate m

he best-supported model included share of old growth stands,hare of uneven-aged stands, and the number of microhabitat trees.he parameters based on model-averaging show that abundancencreased with increasing share of stands with uneven structureTable 7, Fig. 3).

In the case of FDR, two well-supported models (models 4 and; delta AICc ≤ 2 compared to the best model) included structuralariables of forest naturalness, i.e. the number of large living trees,hare of old growth and uneven-aged stands (Table 6). These two

able 5odel coefficients of the environmental variables included in the best ranked models exprawa River (FDR), and Knyszyn Forest (KF).

Area Variable Estimate

FDR* Number of large living trees 0.017

Old growth 0.089

Uneven-aged stands 0.012

Number of microhabitat trees 0.139

Number of snags −0.440

KF Old growth 0.136

Uneven-aged stands 0.086

* Estimation based on model-averaging. See Table 4 for the full list of models tested.

rsimonious models.

models accounted for most Akaike weights (0.77), but the evidenceratio between model-weight of the first-ranked model relative tothe second best model was 2.6. The parameters based on model-averaging show that middle spotted woodpeckers were more abun-dant in plots with greater number of large living trees and share

of old growth stands (Table 7, Fig. 3). Similarly, in the case of KF,the best model (model 4) included both variables mentioned aboveand species’ abundance increased along with the growing numberof large living trees and share of old growth stands (Table 7, Fig. 3).

laining variation in the occurrence of middle spotted woodpeckers in Forest at the

SE 2.5% CL 97.5% CL

0.030 −0.043 0.0760.036 0.018 0.1610.025 −0.038 0.0620.226 −0.315 0.5920.209 −0.860 −0.019

0.058 0.044 0.2770.038 0.018 0.173

838 K. Stachura-Skierczynska, Z. Kosinski / Ecological Indicators 60 (2016) 832–844

Table 6Model-selection statistic for the analysis of the abundance of middle spotted woodpeckers in Krotoszyn Oak Forest (KOF), Forest at the Drawa River (FDR), and KnyszynForest (KF). The best models (separated by �AICc ≤2) are highlighted in bold.

Area Model Model function dfa AICcb �AICcc Weightd

KOF 7 Old growth + Uneven-aged stands + Number of large living trees + Numberof microhabitat trees

5 79.4 0.00 0.426

5 Number of large living trees + Old growth + Uneven-aged stands 4 80.3 0.88 0.2746 Old growth + Uneven-aged stands 3 80.5 1.17 0.237

14 Old growth + Uneven-aged stands + Number of large living trees + Number ofmicrohabitat trees + Number of snags

6 83.7 4.37 0.048

4 Number of large living trees + Old growth 3 87.7 8.29 0.0072 Number of large living trees 2 90.1 10.69 0.002

10 Number of snags 2 90.8 11.43 0.0019 Number of tree species + Number of snags 3 91.7 12.32 0.001

12 Wet habitats 2 91.7 12.36 0.0011 Intercept-only model 1 92.0 12.64 0.001

11 Number of cut stamps 2 92.8 13.39 0.0013 Number of large living trees + Number of microhabitat trees 3 92.9 13.54 0.0008 Number of tree species 2 93.4 14.03 0.000

13 Number of cut stamps + Wet habitats 3 93.8 14.43 0.00015 Number of large living trees + Number of microhabitat trees + Number of snags 4 94.3 14.96 0.00016 Old growth + Uneven-aged stands + Number of large living trees + Number of

microhabitat trees + Number of tree species + Number of snags + Number of cutstamps + Wet habitats

9 101.1 21.68 0.000

FDR 4 Number of large living trees + Old growth 3 130.8 0.00 0.5585 Number of large living trees + Old growth + Uneven-aged stands 4 132.7 1.92 0.2147 Old growth + Uneven-aged stands + Number of large living trees + Number of

microhabitat trees5 135.1 4.31 0.065

14 Old growth + Uneven-aged stands + Number of large living trees + Number ofmicrohabitat trees + Number of snags

6 135.2 4.40 0.062

2 Number of large living trees 2 135.5 4.77 0.05115 Number of large living trees + Number of microhabitat trees + Number of snags 4 137.1 6.39 0.023

3 Number of large living trees + Number of microhabitat trees 3 137.6 6.83 0.0186 Old growth + Uneven-aged stands 3 139.4 8.60 0.008

16 Old growth + Uneven-aged stands + Number of large living trees + Number ofmicrohabitat trees + Number of tree species + Number of snags + Number of cutstamps + Wet habitats

9 141.8 11.02 0.002

10 Number of snags 2 162.0 31.28 0.0009 Number of tree species + Number of snags 3 163.2 32.42 0.0001 Intercept-only model 1 169.1 38.30 0.000

11 Number of cut stamps 2 169.6 38.84 0.00013 Number of cut stamps + Wet habitats 3 170.3 39.56 0.00012 Wet habitats 2 170.5 39.77 0.000

8 Number of tree species trees 2 171.0 40.22 0.000

KF 4 Number of large living trees + Old growth 3 96.2 0.00 0.6365 Number of large living trees + Old growth + Uneven-aged stands 4 98.5 2.28 0.2037 Old growth + Uneven-aged stands + Number of large living trees + Number of

microhabitat trees5 100.7 4.49 0.067

6 Old growth + Uneven-aged stands 3 101.2 5.01 0.05214 Old growth + Uneven-aged stands + Number of large living trees + Number of

microhabitat trees + Number of snags6 101.9 5.74 0.036

16 Old growth + Uneven-aged stands + Number of large living trees + Number ofmicrohabitat trees + Number of tree species + Number of snags + Number of cutstamps + Wet habitats

9 105.6 9.36 0.006

8 Number of tree species 2 133.4 37.17 0.0009 Number of tree species + Number of snags 3 134.0 37.80 0.0002 Number of large living trees 2 136.2 39.97 0.0003 Number of large living trees + Number of microhabitat trees 3 138.5 42.25 0.000

15 Number of large living trees + Number of microhabitat trees + Number of snags 4 140.7 44.53 0.00011 Number of cut stamps 2 144.6 48.39 0.00013 Number of cut stamps + Wet habitats 3 146.4 50.19 0.000

1 Intercept-only model 1 146.5 50.25 0.00012 Wet habitats 2 146.6 50.36 0.00010 Number of snags 2 148.6 52.39 0.000

a Degrees of freedom.b

ore pa

4

ueb

Akaike’s information criterion.c Difference in AICc relative to the model with the lowest AICc.d Models are ranked according to their Akaike weight; higher weights indicate m

. Discussion

Our results show that different elements describing forest nat-ralness in the study areas were frequently independent. Forxample, stands with uneven age structure and high share of old,ig trees were not those with the highest number of snags. Although

rsimonious models.

it is generally assumed that substantial resources of dead woodcoincide with old, unevenly structured forests and high degree of

naturalness (Seidling et al., 2014; Siitonen et al., 2000), dead woodis not a static ecosystem component, and its amount can change intime due to differing disturbance regimes and decay rates (Aakala,2011). For example, young stands regenerating after large-scale

K. Stachura-Skierczynska, Z. Kosinski / Ecological Indicators 60 (2016) 832–844 839

Table 7Model coefficients of the environmental variables included in the best ranked models explaining variation in the abundance of middle spotted woodpeckers in KrotoszynOak Forest (KOF), Forest at the Drawa River (FDR), and Knyszyn Forest (KF).

Area Variable Estimate SE 2.5% CL 97.5% CL

KOF* Old growth −0.033 0.016 −0.067 0.001Uneven-aged stands 0.024 0.009 0.006 0.043Number of large living trees 0.023 0.011 −0.001 0.047Number of microhabitat trees −0.117 0.055 −0.235 <0.001

FDR* Number of large living trees 0.031 0.009 0.012 0.050Old growth 0.026 0.010 0.006 0.045Uneven-aged stands 0.006 0.009 −0.013 0.024

KF Number of large living trees 0.036 0.013 0.011 0.063Old growth 0.062 0.009 0.044 0.081

ted.

dwycshsicm1t(S

sigoe

Ftpl

forest naturalness. In our study, the geographical scope was nar-rower and limited to the zone where deciduous and mixed forests,suitable for middle spotted woodpecker, naturally occur. Moreover,

* Estimation based on model-averaging. See Table 6 for the full list of models tes

isturbances are natural forests, with high volumes of decayingood, even though they have not developed uneven-aged structure

et. On the contrary, forests managed with shelterwood or selectiveutting, with moderately high cutting age, can have quite high den-ities of large trees and display age unevenness, despite their longistory of use. KOF in our study is an example of a forest that haserved as a source of high-quality timber and thus has been quitentensively managed for two centuries. According to the histori-al sources, in the 19th century, forests in KOF have been managedainly by large clear-cutting (Macias and Szymczak, 2012; Switała,

948). Small clear-cutting (3 ha at the maximum) is still in useoday, along with more ecologically oriented silvicultural systemsmainly shelterwood), implemented during last decades (after theecond World War).

Roberge et al. (2008) found high positive correlations betweentructural, compositional, and functional types of variables defin-

ng forest naturalness in sampled forests, distributed along wideeographical gradient, from boreal coniferous to lowland decidu-us communities. It should be noted that sampled regions differedssentially not only in natural habitat conditions and composition

ig. 2. Occurrence probability of the middle spotted woodpecker in relation to habi-at variables in Forest at the Drawa River (upper panel), and Knyszyn Forest (loweranel). Shown are predicted values (solid line), and 95% confidence limits (dashed

ines). For more details, see Table 5.

of native tree species, but also in terms of the usage intensity,ranging from boreal forests, intensively managed with industrialforestry methods to seminatural ecosystems of Central-EasternEurope and almost untouched lowland primeval forest (Angelstamand Donz-Breuss, 2004; Brumelis et al., 2011; Hanski and Walsh,2004). The combination of data from different habitat areas resultedin the high correlation of variables representing various aspects of

Fig. 3. Factors affecting abundance of the middle spotted woodpecker in KrotoszynOak Forest (upper panel), Forest at the Drawa River (middle panel), and KnyszynForest (lower panel). Shown are predicted values (solid line), and 95% confidencelimits (dashed lines). For more details, see Table 7.

8 / Ecol

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40 K. Stachura-Skierczynska, Z. Kosinski

he majority of sample plots were located in managed forests.his explains the fact that structural, compositional, and functionalspects of forest naturalness were generally not correlated andoexisted at one place only in certain cases.

In the two studied areas (KOF was omitted due to small sampleize of absence plots), environmental variables related to the struc-ural elements of forest naturalness, i.e. the share of old growthtands (FDR, KF) and share of stands with uneven-aged structureKF), positively influenced the occurrence of middle spotted wood-ecker. Moreover, the share of old growth and number of large

iving trees (FDR, KF), as well as share of stands with uneven-agedtructure (KOF), positively affected species’ abundance. In both FDRnd KF, old growth stands were assumed as those exceeding theverage cutting age (120 years for oak and other hardwood species).osinski and Winiecki (2005) observed that the increasing share oftands older than 120 years positively affected the middle spot-ed woodpecker population density, which was also confirmed byhis study. However, in KOF, the age limit for old growth was muchigher because of prolonged cutting age for oak in this area (180ears). On the contrary, there is enough old oaks (over 120 years) inOF anyway, providing sufficiently good habitat to maintain popu-

ation density at the level of 1 pair/10 ha (Kosinski and Hybsz, 2006;osinski and Kempa, 2007), and the fact that very old stands (over80 years) occur locally does not have much effect on the species’igh abundances.

Earlier studies suggest that stand structure is a main fac-or determining middle spotted woodpeckers’ presence. Delahayet al. (2010) found that in oak-rich forests, middle spotted wood-eckers select areas with the largest trees (mean DBH = ca. 48 cm)nd high diversity of tree species in the canopy. Robles et al. (2007b)evealed that the presence of middle spotted woodpeckers was pos-tively associated with the density of large oaks (DBH ≥ 37 cm). Inddition, these woodpeckers were more likely to colonize habitatatches with a high density of large oaks (Robles and Ciudad, 2012).alczak et al. (2013, 2014) showed that uneven-aged structure of

anopy layer was among important predictors of the species’ occur-ence. Moreover, it was also seen that besides tree species and age,neven stand structure is a significant predictor of the occurrencef middle spotted woodpecker in forest landscapes (Stachura-kierczynska and Kosinski, 2014). The association of middle spottedoodpeckers’ occurrence with the percentage share of old growth

r number of large living trees is related to the availability of foodn different life stages. Pasinelli and Hegelbach (1997) found thatdult middle spotted woodpeckers highly prefer foraging in large,ld oaks with diameter at breast height between 36 and 72 cm.imilar to the adults, juvenile middle spotted woodpeckers preferarge trees for foraging during the postfledging period (Ciudad et al.,009), which is critical for the survival of this woodpecker speciesRobles et al., 2007a). All together, these studies suggest that a highmount of large rough-bark deciduous trees provide suitable forag-ng condition for the persistence of the middle spotted woodpeckert multiple life stages.

The structural elements of forests are frequently correlated andften coincide; thus, mature and old stands rich in large trees pre-erred by woodpeckers are often (but not always) characterizedy uneven-aged structure. Old deciduous stands can develop suchnevenness due to management practices, e.g. single stem or groupelection and irregular shelterwood cutting (Philip, 1994). Coppice-ith-standard silvicultural system resulting in two-layered forests,ith prevailing mainly large oak in overstory and coppice in thenderstory, can also form a suitable habitat for middle spottedoodpeckers in Central Europe (Pasinelli et al., 2001; Spitznagel,

990). Similarly, silvopastoral management system associated withxtensive grazing in Spain (“Pyrenean oak dehesas”; Robles et al.,007b) and Romania (Dorresteijn et al., 2013), composed of clearedak woodlands with large trees, provide an useful habitat for

ogical Indicators 60 (2016) 832–844

middle spotted woodpeckers. Management systems listed aboverepresent different degrees of naturalness of forest ecosystems.Coppice-with-standards is to be considered the closest to naturallypreserved lowland forest type, e.g. in floodplain forests (Machar,2009). On the contrary, silvopastoral management systems seemto be less natural (Maranón, 1988). Uneven-aged stand structure,associated with the presence of small gaps and openings in thecanopy, as well as several isolated crowns or branches, may offeradditional feeding opportunities and increase occurrence proba-bility of middle spotted woodpeckers. This is due to the fact thatmore sunlight can reach lower branches and tree trunks favoringthe abundance of tree-dwelling arthropods, which is a basic foodfor middle spotted woodpeckers (Pasinelli and Hegelbach, 1997).However, evenly structured Spanish dehesas and wood pastures inRomania (Dorresteijn et al., 2013; Robles et al., 2007b), with scat-tered large oaks, probably offer extraordinary favorable conditionsto arthropods. This can be confirmed by similar nesting success andnestling survival of middle spotted woodpeckers in dehesas andmature forests in Spain and old, semi-natural flood plain forestsin Poland (Kosinski and Ksit, 2006; Robles et al., 2008). Moreover,Robles et al. (2007a, 2008) did not find any significant effects offragmentation on reproductive parameters, as well as postfledg-ing, first-year and adult survival of middle spotted woodpeckersin oak dehesas and mature oak forests. These results suggest thatmiddle spotted woodpeckers exhibit a considerable degree of flex-ibility according to habitat structure and naturalness of inhabitedforest ecosystems across species’ range. It seems that this occur-rence is simply influenced by the availability of sufficient numberof large, rough-barked trees (Kosinski, 2006; Müller et al., 2009;Pasinelli, 2000, 2003; Roberge et al., 2008; Robles et al., 2007a;Robles and Ciudad, 2012), irrespective of the anthropogenic change(hemeroby) of forests. However, there are threshold values of somehabitat parameters (e.g. forest age, basal area of large deciduoustree/oak), under which a high anthropogenic change of forest willprevent woodpecker occupancy (Müller et al., 2009; Roberge et al.,2008).

In our study, the compositional variable describing the numberof snags was not significant, except for FDR where it negativelyinfluenced the presence of species. The latter case might beexplained by the presence of a number of pine snags in FDR,unsuitable for middle spotted woodpeckers. The role of snags inthe ecology of this species has not been explicitly stated. Pasinelli(2000) did not find a significant relationship between the amountof dead wood and the territory size suggesting that this com-ponent does not affect the habitat quality for middle spottedwoodpecker. Pasinelli and Hegelbach (1997) did not observe anypreference toward snags during foraging, although several studiessuggest that dead wood might play some role during the winters(Jenni, 1983; Pettersson, 1983). Moreover, Lovaty (2002) foundhigh densities of species in mature oak forest despite all stand-ing dead wood being removed. On the contrary, a recent studyin Switzerland showed that the occurrence probability of middlespotted woodpeckers increased, as a tendency, with decreasingdistance to closest dead tree (Spühler et al., 2015). The middlespotted woodpecker prefers to excavate holes in infested, weak,partially or completely dead trees, even in the trees as thin as14 cm (Kosinski and Winiecki, 2004; Z. Kosinski unpubl. data),but mean diameters of trees at the height of the hole are muchlarger, and achieves the size of 38–39 cm (Kosinski and Ksit, 2007;Pasinelli, 2003). Results of several studies show positive selectiontoward dead trees as nesting sites in both protected and man-aged riverine forests (Hertel, 2003; Kosinski and Winiecki, 2004;

Kosinski et al., 2006; Wesołowski, 1989). However, in managedforests where large snags are scarce, the majority of middle spot-ted woodpecker holes were found in dead parts of living trees(Pasinelli, 2003), and the fraction of holes located in snags was

/ Ecol

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K. Stachura-Skierczynska, Z. Kosinski

ow, no more than 19% (Kosinski and Kempa, 2007; Kosinski et al.,006; Pasinelli, 2007; Robles et al., 2007b). To conclude, snags cane used for nesting if available, but their presence is not a nec-ssary condition for middle spotted woodpeckers’ occurrence andbundance.

Middle spotted woodpeckers also use trees weakened byathogen infestation, but not displaying signs of decay (Kosinskind Winiecki, 2004). This might explain the fact that the num-er of microhabitat trees (living trees displaying signs of decay,

.e. side necrosis, polyporous fungi, crevices, and hollows) wasot among significant model variables explaining species’ occur-ence and abundance. In KOF, species’ abundance unexpectedlyncreased, as a tendency, with decreasing number of microhabitatrees. It should be noted, however, that the usefulness of micro-abitats as biodiversity indicators is questionable due to a strongbserver effect (Paillet et al., 2015). In our study, some plots werehecked by different observers, and observations had been con-ucted during different stages of foliage development (from earlyay until late August). Both might have influenced the probability

f true detection of microhabitats.The association of middle spotted woodpeckers with the per-

entage share of old growth stands and the number of large livingrees was the most pronounced in the case of species’ abundance.hese elements of forest naturalness positively affected the num-er of territories in FDR and KF. Earlier studies found the highestreeding density of middle spotted woodpeckers (1.5 terr./10 ha)

n mature timber forest (150–200 years old), where oaks withBH > 45 cm represent more than 80% of all trees (Lovaty, 2002).oreover, Kosinski and Winiecki (2005) revealed that the density

f species on large-scale plots is positively related to the share of theldest (>120 years old) deciduous stands. Finally, Pasinelli (2000)eported that the density of large oaks is inversely related to the ter-itory size of middle spotted woodpeckers. The results of our andther studies show that the presence of mature, old growth standsith high density of large trees appear to be the most important

n explaining the breeding density of middle spotted woodpeckers.owever, the models linking the abundance of the middle spot-

ed woodpecker to large deciduous trees and the percentage sharef old growth varies between FDR and KF. For example, the min-mum quantity of large deciduous trees needed to maintain theensity of 1 terr./km2 was approximately twofold larger in FDRompared to KF. It is likely that the presence of number of beechesmong large deciduous trees in FDR decreases the abundance ofiddle spotted woodpeckers. It was found that beech stands are

enerally not accessible for nesting or foraging sites, except for veryld stands (>200 years old) (Hertel, 2003; Kosinski, 2006; Wintert al., 2005). In KOF, where suitable habitats with high densitiesf large oaks are common, woodpeckers were present in almostll study plots. Here, the uneven-aged structure, consisting of ateast 80 years old with at least 30 years of age difference betweenrees, was a main factor influencing the species’ abundance. In ear-ier studies, we found that the share of uneven-aged stands inOF is positively correlated with the oldest growth stage (>120ears old) (Stachura-Skierczynska and Kosinski, 2014). Moreover,s we mentioned earlier, the density of middle spotted wood-eckers is positively related to the share of the oldest deciduoustands (Kosinski and Winiecki, 2005). Thus, the significant effectf uneven-stand structure might simply be a by-product of forestge.

It is noteworthy that none of the functional elements of natu-alness, describing the intensity of forest management (expresseds the number of cut stumps and the share of wet habitats), was

ncluded in the best-fit models. In case of wet habitats, it is pos-ible that their total area within 1 km2 plots was relatively smallnd did not influence model results. As for management prac-ices, cutting of single trees or groups of trees did not influence

ogical Indicators 60 (2016) 832–844 841

woodpeckers’ occurrence probability and abundance. However,due to such practices, more light might reach the lower stories ofthe forest, positively affecting the abundance of insects, which isthe main food source for middle spotted woodpeckers (Pasinelliand Hegelbach, 1997; Pasinelli, 2003). It should be noted, how-ever, that in this study, we did not analyze clear-cutting (standswhere trees were completely removed in one stage) and standsyounger than 20 years because such high-intensity managementwould certainly cause the loss of all suitable habitat and absence ofwoodpeckers.

Roberge et al. (2008) analyzed the landscape-scale patterns ofrelationship between four woodpecker species (including middlespotted woodpecker) and forest naturalness described by quan-tifiable indicators related to the stand structure. In general, highdegree of naturalness, expressed as main principal componentconsisting of several correlated variables, positively affected theoccurrence of all woodpecker species. However, it should be notedthat the study area encompassed forests of Eastern and North-ern Europe, and middle spotted woodpecker was present only intwo out of four sampled areas (northeast Poland and Lithuania).Conifer-dominated forests of Scandinavia are mostly unsuitable formiddle spotted woodpecker due to lack of oak and other rough-barked deciduous species, except for the most southern part of thisarea, where species has become extinct (Mikusinski and Angelstam,2004; Pettersson, 1984). Moreover, Roberge et al. (2008) quan-tified habitat requirements of middle spotted woodpeckers onlyregarding the basal area of living deciduous trees with DBH ≥ 40 cm.They found that 90% probability of the occurrence correspondedto 0.8 m2/ha in Lithuania and 2.6 m2/ha in northeastern Poland.It should be stated that the relationship between middle spot-ted woodpecker and forest naturalness described by Roberge et al.(2008) might result from the fact that in the studied areas, the gra-dient of naturalness increased toward the south, and so did theamount of deciduous forest and the number of large deciduoustrees as well. Although it is generally true that natural forests in latedevelopmental stages contain substantial amounts of large trees,not all forests with high densities of large trees or high share ofdeciduous trees are natural.

5. Conclusion

Results of our study show that middle spotted woodpecker is anindicator of structural elements of forest naturalness in several geo-graphically separated areas located within temperate forest zone.Moreover, the species is not affected by the presence of composi-tional and functional factors defining forest naturalness.

Forest naturalness is a subject of various interpretations,depending on local conditions (McRoberts et al., 2012; Winter,2012) and might be defined by several factors unrelated to thespecies’ habitat requirements and occurrence. Middle spottedwoodpecker breeds in managed forests because “natural” forestshave disappeared from most of its distribution range. Therefore, thespecies could survive only in managed forest that preserved someaspects of structural complexity of its primeval habitats. In Poland,as well as in other parts of Europe, the share of natural or near-natural forests is extremely low (Kostovska et al., 2008; Parviainen,2005). However, mature, unevenly structured forests might occuras a result of sustainable forest management, aimed at preservingthe continuity of old stands and the maintenance of diverse ageand species’ structure, providing suitable habitat condition for thespecies. Therefore, both presence and abundance of middle spotted

woodpeckers can serve as indicators of sustainable forest manage-ment in deciduous forests (Wübbenhorst and Südbeck, 2001). Ourresults also imply that it is possible to simultaneously preservesuitable habitat conditions for middle spotted woodpeckers and

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42 K. Stachura-Skierczynska, Z. Kosinski

aintain the commercial exploitation of a forest with a reason-ble economic return. Responsible forest management must takento account the need to increase the share of old deciduous for-st stands with a number of large living trees, mainly oaks, e.g. byncreasing the felling age.

cknowledgments

We are grateful to Arkadiusz Gawronski, Robert Hybsz, Adamasprzak, Przemysław Zurawlew, Tomasz Tumiel, and Łukasz Wal-zak who contributed to data collection. Jakub Szymkowiak helpedith the statistics. Two anonymous reviewers provided valuable

nd constructive comments on the paper. Forest inventory dataere provided by Krotoszyn and Taczanów State Forest Districts

nd by Poznan University of Life Sciences. The study was funded by

VIF OG US LT

KOF (N = 19)OG 1.503 1.000 0.144 0.346

US 3.031 1.000 0.272

LT 2.065 1.000

MT 2.988

TS 2.651SN 1.758

CS 1.422

WH 1.864

FDR (N = 56)OG 1.981 1.000 0.469 0.704

US 1.683 1.000 0.589LT 2.539 1.000

MT 1.677

TS 1.205

SN 1.308

CS 1.227

WH 1.280

KF (N = 40)OG 1.448 1.000 0.376 0.095

US 1.801 1.000 0.390

LT 1.779 1.000 −MT 1.144

TS 1.275

SN 1.813CS 1.845

WH 1.785

ogical Indicators 60 (2016) 832–844

the Polish Ministry of Science and Higher Education (Grant no. NN304 014439). We thank all these people and institutions.

Appendix A.

Variance inflation factors (VIF) and Spearman’s rank correlationcoefficient for variables included as covariates in models of mid-dle spotted woodpeckers occurrence and abundance in KrotoszynOak Forest (KOF), Forest at the Drawa River (FDR), and KnyszynForest (KF). OG – old growth, US – uneven-aged stands, LT – num-ber of large living trees, MT – number of microhabitat trees, TS –number of tree species, SN – number of snags, CS – number of cutstumps, WH – wet habitats. Significant correlations (P < 0.05) arehighlighted in bold. See Methods and Table 1 for descriptions ofvariables.

MT TS SN CS WH

0.329 0.083 −0.100 −0.357 −0.1780.630 −0.425 0.414 −0.018 −0.3000.646 −0.172 0.279 −0.064 −0.0601.000 −0.130 0.441 0.102 −0.012

1.000 0.071 −0.188 0.5011.000 −0.013 0.219

1.000 −0.0281.000

0.192 0.260 −0.159 −0.136 −0.0620.010 0.134 −0.050 −0.093 0.0350.298 0.128 −0.191 −0.136 −0.2151.000 −0.020 −0.162 −0.006 0.012

1.000 0.124 −0.015 0.0241.000 −0.143 0.129

1.000 −0.3651.000

0.106 0.167 0.089 −0.265 0.1810.080 0.051 −0.097 −0.238 0.1370.245 0.250 −0.165 −0.153 −0.2971.000 0.104 0.111 −0.066 −0.043

1.000 −0.292 0.252 −0.0991.000 −0.543 0.569

1.000 −0.2871.000

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